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This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [ABSTRACT FROM AUTHOR]

The detection of river boundaries is a crucial branch of the intelligent perception of unmanned surface vehicles (USVs), it can be used to determine the driving areas of USVs, and also to ensure driving safety by limiting the effective drivable areas of USVs in the river areas. Aiming to detect the boundaries of incompletely structured river channels, this study proposes a real‐time detection method for river boundaries based on a Light Detection and Ranging (LiDAR) sensor. The point clouds that are disturbed by the water surface noise are filtered firstly, and then the spatial and geometric features are extracted separately from the point cloud detected above the water surface. To prevent the error detection and missing detection, the boundary point information is predicted and calibrated in real time by Extended Kalman Filter (EKF). A planning track generation algorithm for coastal autonomous cruise without relying on high‐precision maps, and a heading and distance adaptive control method by Proportional‐Integral‐Derivative (PID), and different driving line generation methods for driving along the narrow river and wide river are proposed respectively. The experimental data verification of river boundary detection shows that the algorithm is accurate, real‐time, and robust. [ABSTRACT FROM AUTHOR]

Summary: In this article, an optimized formation control algorithm is presented for unmanned surface vehicles (USVs) with collision avoidance and prescribed performance. The prescribed formation geometry is designed in the leader‐follower formation architecture, in which each vehicle tracks its intermediary leader with preserving a desired separation. A prescribed performance control design technique is introduced to guarantee the transient and steady‐state performance specifications on formation errors. Radial basis function neural networks (NNs) are employed to approximate modeling uncertainties including damping terms and unmodeled dynamics. Based on an actor‐critic learning strategy, a reinforcement learning (RL) algorithm is proposed to ensure the optimality of formation control and the specified tracking accuracy simultaneously, in which actor NNs take appropriate control behaviors by interacting with the external environment, and critic NNs evaluate the control performance and generate a reinforcement signal to actor NNs for facilitating the improvement of subsequent behaviors. Stability analysis shows that the proposed optimal formation controller achieves semi‐global uniform ultimate boundedness of closed‐loop adaptive systems with prescribed performance. Comparative simulation results illustrate the effectiveness and superiority of the presented control algorithm. [ABSTRACT FROM AUTHOR]

Summary: Traffic congestion, air pollution, fuel wastage, and car accidents are all exacerbated by increased traffic. Thus, vehicular communications, which refer to information transmission between cars, pedestrians, and infrastructures, have lately gained popularity and been extensively explored due to their enormous potential to enable intelligent transportation and various safety applications. Manually piloted cars and automated vehicles can acquire relevant information via vehicular communications to enhance traffic security and boost entertainment services. The basic concept of automobile clouds was originally published in the literature not long ago, and several suggested structural approaches have been presented in this study thus far. Several academics have concentrated on the structural layout to address various problems and, as a result, satisfy user expectations in order to give dependable services. We examined various vehicular cloud topologies in this study. We also offered a complete summary of current network layer research on allowing efficient vehicle communications and examined specific security, architectural, and reliability concerns in vehicular clouds. Also, the taxonomy of vehicular networks was discussed in terms of the service link between vehicular networks and cloud computing. Ultimately, we discussed the research prospects available. The results showed that security and privacy challenges are among the most important challenges. [ABSTRACT FROM AUTHOR]

This article proposes a robust trajectory tracking controller for underactuated unmanned surface vehicles under disturbances. An adaptive integral terminal super‐twisting algorithm is designed for guidance and control, following a cascade strategy. The properties of such algorithm include practical finite‐time (PFT) convergence, robustness against bounded disturbances/uncertainties with unknown boundaries, and chattering attenuation via adaptive gains. A stability analysis is addressed to prove the PFT stability of the overall system. Moreover, the vehicle is subject to wind, waves, and water currents disturbance models to verify the robustness with numerical simulations. Furthermore, real‐time experiments under an extra payload and environmental perturbations with unknown boundaries validate the performance with a prototype. Finally, a comparison against the adaptive super‐twisting algorithm with conventional sliding surface further demonstrates the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

In order to construct an efficient and reliable global path for unmanned surface vehicles (USVs), this paper proposes a USV global path planning algorithm based on an optimized ant colony algorithm (OACA). The algorithm constructs a USV global path planning model by comprehensively considering energy consumption costs and turning control costs. In order to ensure the adaptability of the model to the task environment, the USV's motion characteristics are analyzed with full consideration of external interference, and on this basis, the characterization of energy consumption costs and turning control costs is completed. In order to obtain the global optimal solution of the model and accelerate the convergence speed of the model, in the initialization process of the ant colony algorithm, the initial pheromone is distributed unevenly by introducing the distance relationship among the intermediate node, the starting point, and ending point to improve the initial search efficiency. In the iterative process of searching for the optimal solution, enhancing the guidance effect of the current optimal solution on the offspring ants by introducing a weight factor to improve the update rule of pheromone, which accelerates the convergence speed of the algorithm. In the last two steps of the deadlock path, the number of lost ants is reduced by introducing a penalty factor to punish the pheromone which can guarantee the diversity of ants, and the algorithm's premature convergence is overcome. The simulation results prove the effectiveness of this algorithm. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this article, subject to both pose and velocity constraints within narrow waters, a self‐learning‐based optimal tracking control (SLOTC) scheme is innovatively created for an unmanned surface vehicle (USV) by deploying actor‐critic reinforcement learning (RL) mechanism and backstepping technique. To be specific, the barrier Lyapunov function (BLF) is devised to uniformly limit the states within a predefined region pertaining to a smoothly feasible reference trajectory. By virtue of a constrained Hamilton–Jacobi–Bellman (HJB) function, an actor‐critic control structure under backstepping is established by employing adaptive neural network identifiers which recursively updates actor and critic, simultaneously. Eventually, theoretical analysis proves that the entire SLOTC scheme can render all the states remain in the predefined compact set while tracking errors converge to an arbitrarily small neighborhood of the origin. Simulation results on a prototype USV demonstrate remarkable effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Summary: The article proposes a robust adaptive framework for platoon of uncertain unmanned surface vehicles (USVs) subject to stochastic environmental loads. The disturbances induced by waves, wind, and ocean currents in the kinetics are decomposed into deterministic and stochastic components. The deterministic components can be treated as unknown constants, while stochastic components are regarded as Gaussian random disturbances. The stochastic additive noises are also included in the kinematics which stands for the un‐modeled dynamics and uncertainty. A comprehensive model including kinematics and kinetics of each USV agent is then derived as stochastic differential equations including standard Wiener processes. Thus, the problem formulation is much more challenging and practical since both the exogenous disturbances and kinematics states are defined by stochastic differential equations. Dynamic surface control technique, quartic Lyapunov functions synthesis, the projection algorithm, and neural networks are employed in order to guarantee that all the tracking errors are semi‐globally uniformly ultimately bounded in probability. Finally, the simulation experiments quantify the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the path following control for an underactuated unmanned surface vehicle (USV) subject to modeling error and input saturation under time‐varying external disturbances. In this note, a novel trajectory linearization control (TLC) method is first introduced in the design of path following controller for an unmanned surface vessel. Adaptive line‐of‐sight (LOS) algorithm is used in the navigation strategy, which not only solves the sideslip angle problem caused by external disturbances but also optimizes the convergence speed. The neural network minimum learning parameter (MLP) method is used to compensate for modeling errors and external disturbances. Compared with multi‐layer neural networks, MLP solves the problem of 'dimension disaster'. Meanwhile, in order to increase the robustness of the system, adaptive technology is employed to compensate for the compensation error of MLP. In addition, an auxiliary dynamic system is introduced into the controller design to address potential input saturation issue. Considering the above practical conditions, it is more convenient for the engineering implementation of the proposed control path following the control system. Using Lyapunov stability analysis theory, it is proven that all error signals in the system are uniformly ultimately bounded. Finally, two simulation experiments are presented to demonstrate the feasibility of the proposed control strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Model reference adaptive control (MRAC) offers the potential to adapt in real‐time to changes in the performance of small unmanned air vehicles. There are significant challenges with their use, however, primarily in the implementation and assurance of long‐term system stability. This paper presents flight test results for a combined model reference adaptive control (CMRAC) law applied to the height control loop of a multirotor. Key features include the implementation of CMRAC with a baseline controller allowing for in‐flight switching between the two; the use of an augmented state to improve the tracking performance and a CMRAC implementation that provides a shorter transient phase, faster parameter convergence, and closer tracking of the desired reference model response when compared with standard MRAC. With the current exponential growth of interest in unmanned air vehicles, the potential benefits of using CMRAC for control system development are significant, particularly for new vehicles with short development and testing phases and in cases where there are significant configuration changes in flight or prior to rapid deployment. [ABSTRACT FROM AUTHOR]

In this article, the lateral path following problem of unmanned ground vehicle with bounded disturbances is studied. A dual‐mode model predictive control (MPC) algorithm based on adaptive event triggered mechanism is proposed. Compared with the existing MPC algorithm, adaptive event triggered model predictive control (AEMPC) can ensure the tracking accuracy and reduce the optimization computation. Feasibility and stability of AEMPC algorithm are guaranteed by constraint region parameter design. Finally, simulation results of the vehicle dynamics model are given, and the comparisons are made. [ABSTRACT FROM AUTHOR]

Electromagnetic loop systems rely on the use of non‐conductive materials near the sensor to minimize bias effects superimposed on measured data. For marine sensors, rigidity, compactness and ease of platform handling are essential. Thus, commonly a compromise between rigid, cost‐effective and non‐conductive materials (e.g. stainless steel versus fibreglass composites) needs to be found. For systems dedicated to controlled‐source electromagnetic measurements, a spatial separation between critical system components and sensors may be feasible, whereas compact multi‐sensor platforms, remotely operated vehicles and autonomous unmanned vehicles require the use of electrically conductive components near the sensor. While data analysis and geological interpretations benefit vastly from each added instrument and multidisciplinary approaches, this introduces a systematic and platform‐immanent bias in the measured electromagnetic data. In this scope, we present two comparable case studies targeting loop‐source electromagnetic applications in both time and frequency domains: the time‐domain system trades the compact design for a clear separation of 15 m between an upper fibreglass frame, holding most critical titanium system components, and a lower frame with its coil and receivers. In case of the frequency‐domain profiler, the compact and rigid design is achieved by a circular fibreglass platform, carrying the transmitting and receiving coils, as well as several titanium housings and instruments. In this study, we analyse and quantify the quasi‐static influence of conductive objects on time‐ and frequency‐domain coil systems by applying an analytically and experimentally verified 3D finite element model. Moreover, we present calibration and optimization procedures to minimize bias inherent in the measured data. The numerical experiments do not only show the significance of the bias on the inversion results, but also the efficiency of a system calibration against the analytically calculated response of a known environment. The remaining bias after calibration is a time/frequency‐dependent function of seafloor conductivity, which doubles the commonly estimated noise floor from 1% to 2%, decreasing the sensitivity and resolution of the devices. By optimizing size and position of critical conductive system components (e.g. titanium housings) and/or modifying the transmitter/receiver geometry, we significantly reduce the effect of this residual bias on the inversion results as demonstrated by 3D modelling. These procedures motivate the opportunity to design dedicated, compact, low‐bias platforms and provide a solution for autonomous and remotely steered designs by minimizing their effect on the sensitivity of the controlled‐source electromagnetic sensor. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (BSs) to provide auxiliary communication services. In this letter, we propose a deep reinforcement learning (DRL)‐based dynamic deployment method for multi‐UAV communications. The phasic policy gradient (PPG) is designed to improve the sample efficiency and the attention of the multi‐UAV deployment. Simulation results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses three interrelated problems concerning the path following of a podded propulsion unmanned surface vessel (USV), namely, the modeling of podded propulsion USV, the model parameter identification and the guidance law design. Based on hypotheses and simplification, three degree of freedom (DOF) manipulating mathematical model group (MMG) models of podded propulsion USV are reduced to the response model, and then, a series of field experiments (turning experiment and zig‐zag experiment) is carried out to collect corresponding data. On this basis, the parameters of the model are obtained through the method of system identification. Meanwhile, the simulation data are compared with the actual data to verify the accuracy of the identification results. The conventional line‐of‐sight guidance is modified by adaptive sideslip angle and a time‐varying lookahead distance, where the lookahead distance is adjusted by fuzzy algorithm. Lyapunov function is used to prove that all error signals in the system are uniformly ultimately bounded. Three numerical examples and the real ship field experiment have been described to illustrate the performance and effectiveness of the proposed scheme. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we consider the problem of coordinating a collection of autonomous unmanned vehicles while guaranteeing collision avoidance. Each vehicle is regulated by a local controller that ensures stability and provides desired path tracking performance in the absence of constraints. The fulfillment of coordination tasks (e.g., collision avoidance) and local constraints (e.g., input saturation constraints) is achieved through a command governor (CG) strategy that, whenever necessary, modifies the nominal paths of the vehicles. First, a centralized CG approach is proposed and fully analyzed. Then, a more interesting distributed implementation requiring low communication rates is discussed. Both approaches make use of a receding horizon strategy and require the on-line solution of mixed-integer optimization programs. Finally, an example is given for illustration purposes. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A path planning approach is proposed that is in accordance with Collision Regulations (COLREGS) for avoidance of a potential collision and considers the time-varying trajectory uncertainties of an autonomous surface vehicle (ASV) and an obstacle. Unfortunately, as the motion information for each object includes various uncertainties caused by characteristics of the navigation and perception systems, the trajectory uncertainties increase linearly during the prespecified time in a trajectory prediction process. This trajectory uncertainty is preferentially modelled using the error covariance of a tracking filter designed for estimating motion information. Subsequently, collision risk is computed using a probabilistic method, and a collision risk zone (CRZ) is configured on the predicted trajectory. Finally, a COLREGS-compliant path is planned with consideration of the dynamic characteristics of the ASV to avoid the CRZ. Simulation results that demonstrate the feasibility of the proposed approach with marine traffic examples are presented and discussed. [ABSTRACT FROM AUTHOR]